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PINT: Maximum-likelihood estimation of pulsar timing noise parameters

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arxiv 2405.01977 v2 pith:MRLLHOIU submitted 2024-05-03 astro-ph.IM astro-ph.HE

PINT: Maximum-likelihood estimation of pulsar timing noise parameters

Abhimanyu Susobhanan , David Kaplan , Anne Archibald , Jing Luo , Paul Ray , Timothy Pennucci , Scott Ransom , Gabriella Agazie
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William Fiore Bjorn Larsen Patrick O'Neill Rutger van Haasteren Akash Anumarlapudi Matteo Bachetti Deven Bhakta Chloe Champagne H. Thankful Cromartie Paul Demorest Ross Jennings Matthew Kerr Sasha Levina Alexander McEwen Brent Shapiro-Albert Joseph Swiggum
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PINT is a pure-Python framework for high-precision pulsar timing developed on top of widely used and well-tested Python libraries, supporting both interactive and programmatic data analysis workflows. We present a new frequentist framework within PINT to characterize the single-pulsar noise processes present in pulsar timing datasets. This framework enables the parameter estimation for both uncorrelated and correlated noise processes as well as the model comparison between different timing and noise models in a computationally inexpensive way. We demonstrate the efficacy of the new framework by applying it to simulated datasets as well as a real dataset of PSR B1855+09. We also describe the new features implemented in PINT since it was first described in the literature.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures

    astro-ph.IM 2026-07 unverdicted novelty 6.0

    PSRDISP characterises deterministic and stochastic dispersive processes via Gaussian processes on epoch-wise DM estimates, recovering injected signals on simulated narrowband and wideband pulsar timing data.

  2. PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures

    astro-ph.IM 2026-07 conditional novelty 6.0

    PSRDISP is a Gaussian-process framework that fits dispersion-measure and solar-wind noise directly to epoch-wise dispersion measures, recovering injected signals in simulated pulsar data.

  3. PSRDISP: A novel approach to modeling dispersive processes in single-pulsar noise analysis using epoch-wise dispersion measures

    astro-ph.IM 2026-07 conditional novelty 5.0

    A Fourier-domain Gaussian process fit to epoch-wise pulsar dispersion measures recovers injected DM and solar-wind noise in simulated narrowband and wideband timing data.

  4. Constraints on Einstein-aether gravity from the precision timing of PSR J1738+0333

    gr-qc 2026-05 unverdicted novelty 4.0

    Precision timing of PSR J1738+0333 from EPTA and NANOGrav data yields the tightest strong-field constraints on Einstein-aether parameters from any single binary pulsar.